In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this article, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such data, we propose an outcome weighted learning method based on a nonconvex loss function, which can be solved efficiently using a difference of convex functions algorithm. The consistency and convergence rate for the estimated IDR are derived, and its small-sample performance is evaluated via simulation studies. We demonstrate that the proposed method outperforms competing approaches. Finally, we illustrate this method using data from a cohort study for warfarin (an anti-thrombotic drug) dosing. Supplementary materials for this article are available online.
Rheumatoid arthritis (RA) is a systemic immunodeficiency disease characterized by persistent synovial inflammation, pannus formation, and bone and cartilage destruction, resulting in joint malformations and function decline.The purpose of this study is to evaluate the effect of moxibustion on clinical symptoms and levels of pain-related indicators beta-endorphin (β-EP) and dynorphin (Dyn) in patients with RA and to explore the potential anti-inflammatory and analgesic mechanisms of moxibustion in RA treatment.A total of 64 patients with RA who met the inclusion criteria were randomly and equally classified into the control and treatment groups. The control group received conventional treatment (oral methotrexate, folate, or leflunomide prescribed for a long time). The treatment group was treated with moxibustion at ST36 (Zusanli), BL23 (Shenshu), and Ashi points with respect to the control group. Patients' clinical symptoms and routine inspection indexes (rheumatoid factor [RF], erythrocyte sedimentation rate [ESR], and C-reactive protein [CRP]) were recorded before and after treatment. Serum levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), β-EP, and Dyn were determined by enzyme-linked immunosorbent assay (ELISA). The software SPSS24.0 was used for statistical analysis.(1) Compared with the pretreatment result, both of the two groups' clinical symptoms and routine inspection indexes (RF, ESR, and CRP) improved (P < 0.05), and the improvement of clinical symptoms in the treatment group outperformed that in the control group (P < 0.05). (2) TNF-α and IL-1β levels decreased significantly in the treatment group after treatment (P < 0.01), while no significant difference was observed in the control group (P > 0.05). (3) β-EP and Dyn levels in the treatment group were significantly increased after treatment (P < 0.01, P < 0.01), but the control group showed no significant difference (P > 0.05, P > 0.05). It is worth mentioning that the serum TNF-α, IL-1β, β-EP, and Dyn levels between the two groups were significantly different after 8 weeks of treatment (P < 0.05). (4) Differences in the serum β-EP and Dyn levels in the patients of the treatment group were correlated with TNF-α and IL-1β levels after treatment, and the correlation was mainly negative (r < 0).Moxibustion can improve joint pain in patients with RA using conventional western medicine. One of the mechanisms may affect the serum β-EP and Dyn levels by downregulating the inflammatory factors to play an anti-inflammatory and analgesic role.
Health care payments are an important component of health care utilization and are thus a major focus in health services and health policy applications. However, payment outcomes are semicontinuous in that over a given period of time some patients incur no payments and some patients incur large costs. Individualized treatment rules (ITRs) are a major part of the push for tailoring treatments and interventions to patients, yet there is a little work focused on estimating ITRs from semicontinuous outcomes. In this article, we develop a framework for estimation of ITRs based on two-part modeling, wherein the ITR is estimated by separately targeting the zero part of the outcome and the strictly positive part. To improve performance when high-dimensional covariates are available, we leverage a scientifically plausible penalty that simultaneously selects variables and encourages the signs of coefficients for each variable to agree between the two components of the ITR. We develop an efficient algorithm for computation and prove oracle inequalities for the resulting estimation and prediction errors. We demonstrate the effectiveness of our approach in simulated examples and in a study of a health system intervention. Supplementary materials for this article are available online.
Abstract Applications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
Outliers are common in data collection applications with wireless sensor networks, which consist of a large number of sensor nodes, embedded in physical space. The limited power supplies and noisy sensor data put challenges for outlier detection and cleaning in sensor networks. In this paper, we propose utilizing spatial and temporal dependencies that exist sensory readings. Our approach is based on Kalman filter and we design the state transition module and measuring module of the Kalman filter to exploit the temporal and spatial dependencies of sensor data respectively. The experimental results illustrate the effectiveness of our approach.
Abstract The escalating number of dengue virus (DENV) outbreaks and their worldwide spread pose a major threat to global public health. DENV transmission dynamics significantly influence outbreak duration and magnitude. Conventional DENV transmission requires an incubation period between mosquitoes biting infected humans and the mosquitoes becoming infectious. However, the possibility of immediate, mechanical transmission of DENV without viral replication in the mosquito has received little attention despite its potential importance. Here, we show that Aedes aegypti mosquitoes can mechanically transmit DENV to susceptible mice immediately after biting infected mice without the need for an incubation period. By incorporating parameters from our experiments into a newly developed mathematical model, we found a significant impact on DENV outbreak characteristics. Mechanical transmission may amplify existing disease transmission routes and influence outbreak dynamics. Our findings have implications for vector control strategies that target mosquito lifespan and suggest the possibility of similar mechanical transmission routes in other disease-carrying mosquitoes.
The epidemiology of depression in patients with psoriasis has not been well defined in the Asian population. This study evaluated the epidemiological features of, and risk factors for, depression among patients with psoriasis in Taiwan. A nationwide population-based cross-sectional study was undertaken using the National Health Insurance Research Database. This study included 17,086 patients with psoriasis and 1,607,242 patients from the general population. The prevalence of depression in patients with psoriasis was 11.52%, while the prevalence of depression in the general population was 7.73% (prevalence ratio 1.49, 95% confidence interval 1.43-1.55). Multivariable analysis showed that, in patients with psoriasis, risk factors associated with depression were: age 20-50 years, female sex, low income, and major comorbid diseases, including liver cirrhosis, renal disease, cardiovascular disease and cerebrovascular disease. Therefore, the prevalence of depression is higher in patients with psoriasis, particularly in young and middle-aged women with low income and major comorbidities.
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